An agent-based framework for prioritizing building retrofits
Bibliographic record
Abstract
As the urgency to address climate change grows, municipalities face the challenge of lowering carbon emissions from buildings, which account for a large portion of total emissions. However, many cities lack the tools and data required to develop effective policies. This study proposes a practical framework for solving this by creating a user-friendly dashboard tailored to the needs of decision-makers in municipalities. The framework analyses current energy consumption, carbon emissions and building characteristics by leveraging existing datasets such as energy assessment databases and the property tax databases. Decision-makers can visualize the potential impact of various retrofit alternatives using scenario analysis and policy simulation, anticipate future construction rates and analyze the embodied carbon impact. The framework provides insights into the current carbon status and targets and enables municipalities to effectively identify and prioritize climate solutions. This paper applies the framework to single-family houses in the City of Victoria, British Columbia, Canada, however its flexibility enables adaption to other contexts around the world. This study adds to the expanding discussion about municipal climate action by proposing a practical, comprehensive approach to inform policy decisions and expedite progress towards carbon reduction targets. • A data-driven framework for municipal carbon reduction strategies. • Combines building assessment and property tax data for detailed emissions analysis. • Uses agent-based modeling to simulate real-world retrofitting behaviors. • Features a dashboard for policymakers to model retrofit scenarios interactively. • Prioritizes retrofits by actual energy-saving potential for tailored interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".